JMIR Medical Education

Programmatic assessment supports flexible learning and individual progression, but challenges educators to develop frequent assessments reflecting different competencies. The continuous creation of large volumes of assessment items, in a consistent format, in a comparatively restricted time, is laborious. To address this challenge, the application of technological innovations, including artificial intelligence (AI), has been tried. A major concern raised is the validity of the information produced by AI tools, and if not properly verified, can produce inaccurate and therefore inappropriate assessments.

Understanding the roles and patient management approaches of the entire oncology team is imperative for effective communication and optimal cancer treatment. Currently, there is no standard residency or fellowship curriculum to ensure delivery of fundamental knowledge and skills associated with oncology specialties with which trainees often collaborate.

Artificial intelligence (AI) systems are becoming increasingly relevant in everyday clinical practice, with FDA-approved AI solutions now available in many specialties. This development has far-reaching implications for doctors and the future medical profession, highlighting the need for both practicing physicians and medical students to acquire the knowledge, skills, and attitudes necessary to effectively use and evaluate these technologies. Currently, however, there is limited experience with AI-focused curricular training and continuing education.

Alzheimer’s disease (AD) presents significant challenges to healthcare systems worldwide. Early and accurate diagnosis of AD is crucial for effective management and care to enable timely treatment interventions that can preserve cognitive function and improve patient quality of life. However, there are often significant delays in diagnosis. Continuing medical education (CME) has enhanced physician knowledge and confidence in various medical fields, including AD. Notably, web-based CME has been shown to positively influence physician confidence, which can lead to changes in practice and increased adoption of evidence-based treatment selection.

Standardized patients (SPs) have been crucial in medical education, offering realistic patient interactions to students. Despite their benefits, SP training is resource-intensive, and access can be limited. Advances in artificial intelligence, particularly with large language models like ChatGPT, present new opportunities for virtual SPs, potentially addressing these limitations.

Medical students face high levels of burnout and mental health issues during training. Understanding associated factors can inform supportive interventions.

Telemedicine is a key element of modern healthcare, providing remote medical consultations and bridging the gap between patients and healthcare providers. Despite legislative advancements and pilot programs, the integration of telemedicine education in Romania remains limited. Addressing these educational gaps is essential for preparing current and future medical professionals to effectively use telemedicine technologies.

Chat Generative Pre-Trained Transformer (ChatGPTTM) is a large language model (LLM)-based chatbot developed by OpenAITM. ChatGPT has many potential applications to healthcare, including enhanced diagnostic accuracy and efficiency, improved treatment planning, and better patient outcomes. Healthcare professionals’ perceptions of ChatGPT and similar artificial intelligence tools are not well known, and understanding these attitudes is important to inform the best approaches to explore their use in medicine.

Opportunities to learn ultrasound-guided/-assisted (USGA) neuraxial techniques in pediatric patients are limited, given the inherent high stakes and small margin of error in this population. Simulation is especially valuable in pediatrics, because it enhances competency and efficiency, without added risk, when learning new skills, specifically those seen with ultrasound-guided regional anesthetic techniques. However, access to simulation opportunities using phantom models in medical education is limited due to excessive costs. We describe a process to produce ultrasound phantoms, using synthetic ballistic gelatin, that can be used for simulation and are affordable, reproducible, and shelf stable indefinitely. The ultrasound images produced by these phantoms are comparable to those obtained from a real pediatric patient, including sacral anatomy necessary for caudal epidural blocks, as validated by practicing pediatric anesthesiologists. Phantom models offer a more cost-effective alternative to commercially prepared phantoms, expanding access to realistic simulation for neuraxial ultrasound in pediatric medical education without the prohibitively high expense.

Emergency obstetric simulation-based training has increasingly been used to improve emergency obstetric care provision in sub-Saharan Africa. For determining the optimal methodology for effective training sessions in resource-constrained settings, it is crucial to conduct high-quality research.

Studies confirm a relationship between learning style and medical career choice in the learning style patterns observed in distinct types of residency programs. Such patterns can also be applied to general surgery, from medical school to the latest stages of training. Aligning teaching strategies with the predominant learning styles in surgical residency programs has the potential to make training more effective.

During the COVID-19 lockdown, it was difficult for residency training programs to conduct on-site, hands-on training. Distance learning, as an alternative to in-person training, could serve as a viable option during this challenging period, but few studies have assessed its role. This study aims to investigate the impact of distance learning during the lockdown on residents’ self-assessed competency development and to explore the moderating effect of poor mental health on the associations.